Pallet wrapper and imaging system

ABSTRACT

A pallet wrapper and imaging system a pallet loaded with containers to calculate a sustainability based upon a type of the pallet and the containers on the pallet. The system also determines the stability of the pallet and containers, a speed at which the pallet and containers can be wrapped, and an amount of stretch film needed to secure the load for transport. After validation by the imaging system (with or without a wrapper), a QC bot assists a QC worker in remedying or checking any detected errors or potential errors.

BACKGROUND

Various types of pallet wrappers are known to wrap stretch film orstretch wrap around stacked items on a pallet. In one type of palletwrapper, the loaded pallet is placed on a turntable that rotatesrelative to a roll of stretch film. As the turntable is rotated, thestretch film is dispensed about the loaded pallet. The pallet wrapperchanges the height of the roll as the turntable rotates, so that thestretch film is wrapped about the entire or substantially the entireheight of the loaded pallet.

In another type of pallet wrapper, the loaded pallet is stationary andthe pallet wrapper moves about the loaded pallet, unrolling the stretchfilm about the items on the pallet. Again, the height of the roll ischanged as the pallet wrapper travels around the pallet so that all orsubstantially all the height of the pallet and items is wrapped. Othervariations are also known.

SUMMARY

The present disclosure provides a pallet wrapper and imaging system withseveral improvements that can be practiced independently of one another,and some of which could be performed with or without the wrapper.

First, the system images a pallet loaded with items to calculate asustainability based upon a type of the pallet and the items on thepallet. This could be done as part of or independently of the wrapper.This could be done as part of or independently of the validation.

Second, the system images the pallet loaded with items to determine thepallet stability and the minimum amount (or appropriate amount) ofstretch film needed to secure the load for transport.

Third, after validation by the imaging system (with or without awrapper), a QC bot assists a QC worker in remedying or checking anydetected errors or potential errors.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of one possible implementation of a palletwrapper and imaging system.

FIG. 2 shows an example user interface screen indicating a highsustainability score of the loaded pallet.

FIG. 3 shows an example user interface screen indicating a lowsustainability score of the loaded pallet.

FIG. 4 shows an example user interface screen indicating current andpast sustainability scores or percentages from an overall, regional orlocal level.

FIG. 5 shows an example user interface screen that assists the user inimproving the sustainability of the company's operations locally,regionally or overall.

FIG. 6A is a flow chart showing one possible method for determiningstability of a loaded pallet.

FIG. 6B shows an example loaded pallet with low stability.

FIG. 6C shows an example loaded pallet with high stability.

FIG. 7A shows an example user interface screen and a wrapper wrapping aloaded pallet based upon a stability analysis of the loaded pallet.

FIG. 7B shows the example interface and wrapper of FIG. 7A wrapping alow stability loaded pallet.

FIG. 8 shows a first phase of a QC bot implemented on a tablet.

FIG. 9 shows the QC bot implemented with smart glasses.

FIG. 10 shows the QC bot implementation of FIG. 9 , looking through thesmart glasses.

FIG. 11 shows the QC bot implemented with a projector.

FIG. 12 shows images projected onto a loaded pallet by the projector ofFIG. 11 .

FIG. 13 shows the verified loaded pallets being delivered to a store.

FIG. 14 shows a portion of the audit history on the delivery person'smobile device of FIG. 13 .

DETAILED DESCRIPTION

FIG. 1 shows an example pallet wrapper and imaging system 10 accordingto one possible embodiment in a warehouse 11. The system 10 includes apallet wrapper 12 having a turntable 14 and at least one camera 16directed toward the area above the turntable 14. A weight sensor (notvisible) may be in or under the turntable 14 for measuring weight on theturntable 14.

Lights 18 direct illumination toward the area above the turntable 14 toassist the camera 16. A roll of stretch film 20 is mounted to a tower 22adjacent the turntable 14. As is known, the roll of stretch film 20 ismounted to be moved vertically on the tower 22, such as by a motor (notshown), while the turntable 14 rotates.

A user interface 24, such as a touchscreen, is mounted on or near thetower 22. A computer 26 includes at least one processor and storagewhich stores instructions that when executed by the processor performthe functions described herein. The computer 26 receives images from thecamera 16, weight data from the weight sensor, communicates with theuser interface 24, and controls the turntable 14 and lights 18.

The computer 26 sends all collected data to a server 30, which could bea cloud computer that also receives the same data from other suchsystems 10 in the same warehouse 11 and such systems 10 in otherwarehouses 11 a-11 n in other geographic locations around the world. Theserver 30 also includes at least one processor and storage which storesinstructions which when executed by the processor perform the functionsdescribed herein. The server 30 also stores at least one machinelearning model, and preferably a plurality of machine learning models 31a-c trained on images of the items, i.e. the packages of beveragecontainers described below. For example, the machine learning models 31a-c can be trained by manually labeling images of the available packagesof beverage containers.

Alternatively, there could be a DC computer at each warehouse thatperiodically receives a copy of all the machine learning models 31 a-cfrom the server 30. The computer 26 could send all the collected data tothe DC computer, which would analyze the data based upon the machinelearning models as described herein. The data may also be synced to theserver 30 for the comparisons between warehouses 11, 11 a-n as describedbelow. The tasks described herein, or portions thereof, could beperformed by any of the different computers described herein.

In use, a pallet 50, which could be a half-pallet or a full-size pallet,is loaded with items such as packages of beverage containers. Forexample, an item may be a plastic bottle crate 52 (secondary packaging),each containing primary packaging, such as a plurality of bottles 54. InFIG. 1 , a plurality of plastic bottle crates 52 are each loaded withplastic bottles 54. The loaded bottle crates 52 are stacked on oneanother and on a plastic half-pallet 50. The loaded pallet 50 is placedon the turntable 14 (or otherwise in the field of view of the at leastone camera 16).

The computer 26 controls the turntable 14 and the camera 16 so that theturntable 14 rotates and the camera 16 takes one or more images of theloaded pallet 50. Preferably, the camera 16 takes an image of each ofthe four sides of the loaded pallet 50. The computer 26 receives theimages of the loaded pallet 50.

The assignee of the present application has developed a validationsystem that uses machine learning to identify skus of the items on thepallet 50. This is disclosed more fully in US20220129836, filed Oct. 22,2021, assigned to the assignee of the present application and which ishereby incorporated by reference in its entirety. Briefly, as describedin previous patents, the server 30 receives a plurality of orders 33 andpresents a pick list of skus to the user, indicating which items toplace on each pallet 50. A worker places the items (e.g. the plasticbottle crates 52 with the plastic bottles 54) on the pallet 50 accordingto the pick list and places the loaded pallet 50 on the turntable 14 forvalidation and wrapping.

The computer 26 identifies the skus of the items on the pallet 50. Inone implementation, the packaging type of each item on the pallet 50(which in this example is a known/expected combination of both thesecondary packaging and the primary packaging) is first identified usingone machine learning model 31 a to analyze the images of the loadedpallet 50. The package types may include, just as illustrative examples,plastic beverage crate with 24 20 oz plastic bottles, corrugatedcardboard box, cardboard tray with 24 20 oz plastic bottles and plasticoverwrap, cardboard box holding 36 12 oz aluminum cans, and others. The“brand” (i.e. the specific content, such as the flavor and type of thebeverage) is then identified using another machine learning model 31 b(which has been selected from among a plurality of brand machinelearning models based upon the identified package type) to analyze theimages of the loaded pallet 50. The computer 26 then compares theidentified skus to the skus on the order/pick list and generates alertsfor any mismatches.

Sustainability

In one example implementation, the images are also sent to anothermachine learning model 31 c where the package type of every item isidentified, such as plastic beverage crate with plastic bottles oraluminum cans, corrugated cardboard box, cardboard tray with plasticoverwrap, cardboard box holding aluminum cans, and others. In thisexample application, since it is expected that the primary containerwill always be aluminum cans or plastic bottles, the focus here is onthe secondary packaging, i.e. reusable crate versus one-way cardboardtray or one-way cardboard box. Additionally, information regarding thepallet 50 itself is also determined by the machine learning model 31 c,for example, whether the pallet a reusable plastic pallet or is aone-way pallet (e.g. wood pallet). Alternatively, the package typedeterminations made by the machine learning models 31 a that were usedto identify the skus of the items on the pallet 50 could be used.

In this implementation, the system 10 also generates informationregarding sustainability based upon the identified package types andpallet types. This information may be displayed to the user immediatelyand/or may be accumulated by the computer 26 and provided in summaryform. For example, the pallet 50 and secondary packaging 52 (andoptionally the primary packaging, such as the bottles 54) are assessedfor sustainability based upon specific criteria, such as whether theyare one-way or reusable and/or recyclable, or how much of each packageis one-way versus reusable/recyclable. For example, a beveragecontainers may be packaged in a cardboard (one-way) ‘tray’ or ‘box’ withplastic ‘overwrap’ (one-way), or the beverage containers may be packagedin plastic beverage crates (reusable and eventually recyclable). Thepallet 50 may be plastic (reusable and eventually recyclable) or wood(one-way).

The server 30 accumulates the information regarding sustainability forall items on all pallets 50 in that warehouse and in other warehouses.

The system 10 may perform a life cycle analysis of the pallet andpackaging, generally containing the following information:

1. Cradle to Gate—As it drops out of injection molding machine how manyCO2 kg are emitted from the manufacture of that product

2. For the manufacturer—How many times is it used, what distances,servicing needs, etc.

3. Incremental benefits of using the reusable packaging

Category 1—“CO2 avoidance” or Category 2—“waste elimination”

FIG. 2 shows a screen 32 that the system 10 may be presented to the userby the server 30, either on the user interface 24, or on another userinterface, indicating a loaded pallet 50 with a high sustainabilityscore (in this example, 100%). The loaded pallet 50 in FIG. 2 is thesame as the loaded pallet 50 of FIG. 1 . The sustainability score ishigh because the pallet 50 is plastic which is reusable many times andeventually recyclable and because all the secondary packaging is plasticbeverage crates 52, which can also be reused many times and theneventually recycled. Again, the beverage bottles 54 inside the crates 52are always recyclable.

FIG. 3 shows a similar screen 34 that the server 30 may present to auser but indicating a loaded pallet 50 with a low sustainability score(in this example, 25%). In this example, the pallet 50 is wood, which isnot recyclable and is eventually discarded. Most of the secondarypackaging 52 on this pallet 50 is cardboard, which is not reusable. Inthis example the upper layer of items on the pallet 50 comprises plasticreusable beverage crates, which is the portion of this loaded pallet 50that is sustainable.

FIG. 4 shows a screen 38 that may be presented by the server 30 to auser to present current and past sustainability scores or percentagesfrom an overall, regional or local level. The user can use this data todetermine which regions and which facilities are doing well withsustainability and which regions and facilities need to improve thesustainability of their operations. The system 10 can also show changesover time overall, regionally, and locally, so the user can trackimprovement in sustainability. For example, FIG. 4 shows a graphcomparing the sustainability of six different warehouses in a geographicregion. FIG. 4 also shows an overall sustainability rating (65%) forthat geographic region.

Referring to FIG. 5 , the server 30 may also present a screen 40 thatcan assist the user in improving the sustainability of the company'soperations locally, regionally or overall. For example, the user cansend a request to the server 30 asking how one or more changes wouldaffect the sustainability score. Upon request the by the user, thesystem 10 can show the user on the screen 40 how switching from onepackage to another will improve sustainability. In the example shown,the system 10 indicates that sustainability would improve 65% byswitching from one-way cardboard trays to reusable beverage trays for 2412 oz bottles.

Wrapper Optimization

Using the images from the camera 16, the computer 26 may also optimizethe wrapping process. Referring to FIG. 1 and the flow chart of FIG. 6A,in step 110 the camera 16 takes one or more images of the loaded pallet50 prior to initiating wrapping of the loaded pallet 50. As an example,the turntable 14 may rotate such that the camera 16 can image each ofthe four sides of the loaded pallet 50. The computer 26 analyzes theimages prior to wrapping the loaded pallet 50, optionally includingvalidating the skus of the items on the pallet 50 against the associatedorder and rating the sustainability, as above.

The server 30 also analyzes the images of the loaded pallet 50 todetermine the stability of the loaded pallet 50. In step 112, the server30 detects the package faces (such as by edge detection), includingbounding boxes for each package, including the pixel coordinates of eachof the bounding boxes. In step 114, the server 30 determines whatpackage types are in each location on the pallet 50. Steps 112 and 114may already be performed for validation and/or sustainability. Theserver 30 has identified the package type and location of every item onthe pallet 50.

Based upon the coordinates of the bounding boxes of the package faces,the server 30 can determine gaps between adjacent packages in step 116.The server 30 may also analyze the coordinates of the bounding boxes todetect height discrepancies between items in a layer on the pallet 50,which would decrease stability. Additionally, the server 30 may analyzethe overall height of the loaded pallet 50 in terms of absolute heightand/or number of layers or some combination of both, because a tallerstack of items on a pallet 50 is less stable than a short stack of itemson a pallet 50, and more layers of items is less stable than fewerlayers.

In step 118, the server 30 evaluates layer alignment. For example,column stacked items (i.e., each item in one layer is stacked on oneitem in the layer below) is less stable than cross-stacked layers or“brick-stacked” layers in which each item in a layer is stacked on morethan one item in the layer below (e.g. half on one item and half onanother item).

In step 120, the server 30 evaluates the interfaces between adjacentlayers. The server 30 analyzes the stability of each interface betweenthe layers in the stack, i.e. between each layer of items and betweenthe bottom layer of items and the pallet 50. Some package types are verystable if on the top layer, but less stable if other items are stackedon top of them. The package type and the locations of the package typesare factors in the stability of the loaded pallet 50 prior to wrapping.For example, the tops of bottles in plastic crates may be received inrecesses in the bases of similar crate stacked thereon, created a verystable interface between those layers. Cardboard boxes on the other handhave smooth upper surfaces and smooth lower surfaces and thereforeprovide a less stable interface between layers.

In step 122, the server 30 determines the stability of the loaded pallet50 based upon the evaluation of the layer interfaces 120, themeasurements of the package gaps 116, and the evaluation of the layeralignment 118.

Based upon the determined level of stability, in step 126, the server 30may send information to the computer 26 indicating how much stretch film20 to use to wrap the loaded pallet 50. The loaded pallet 50 must bestable, but unnecessary wrapping will waste stretch film 20 and takemore time on the wrapper (more turns of the turntable 14), which reducesefficiency. Therefore, determining the proper amount of stretch film 20is beneficial. The computer 26 may also determine how/where to place thestretch film 20 (e.g. more at the top of the stack and less at thebottom of the stack, or vice versa).

Additionally, in step 124, based upon the level of stability of theloaded pallet 50, the computer 26 also determines how fast the turntable14 can be rotated safely. Rotating the turntable 14 faster reduces thetime necessary to wrap the loaded pallet 50, which increases efficiencyin the warehouse; however, if the loaded, unwrapped pallet 50 is not yetstable, the turntable 14 cannot be rotated too fast or the items mayfall off the pallet 50 or shift.

The server 30 sends the turntable rate 124 and the amount and/or patternof stretch film placement to the wrapper 12.

Again, functions described as being performed on the server 30 couldalternatively be performed in whole or in part on the computer 26 (or aDC computer at each warehouse 11) and vice versa.

FIG. 6B shows an example loaded pallet 50 with low stability. Images ofthe packages 52 are analyzed and bounding boxes 56 of all the packages52 are determined (only three shown) in step 112. Package types 114 areinferred in step 114 to be cardboard boxes and the interfaces betweenthese layers is evaluated in step 120, which would be lower stability.Based upon the coordinates of the bounding boxes 56 of the packages 52,the server 30 determines that there are large gaps g between twopackages 52 in the bottom layer (step 116). The server 30 alsodetermines that there is a height discrepancy h between two adjacentpackages 52 in the bottom layer. Additionally, in step 118, the server30 determines based upon the coordinates of the bounding boxes that thepackages 52 are column-stacked, which is less stable. Therefore, theserver 30 determines in step 122 that the loaded pallet 50 has lowstability and that the turntable rate 124 should be low and that a largeamount of stretch wrap should be used.

In FIG. 6C, an example loaded pallet 50 is shown with high stability.Images of the packages 52 are analyzed and bounding boxes 56 of all thepackages 52 are determined (not shown) in step 112. Package types 114are inferred in step 114 to be cardboard boxes and the interfacesbetween these layers is evaluated in step 120, which would reducestability. Based upon the coordinates of the bounding boxes 56 of thepackages 52, the server 30 determines that there are no large gapsbetween packages 52 (step 116). The server 30 also determines that thereare no significant height discrepancies between adjacent packages 52.Additionally, in step 118, the server 30 determines based upon thecoordinates of the bounding boxes that the packages 52 are brick-stackedand cross-stacked. Therefore, the server 30 determines in step 122 thatthe loaded pallet 50 has relatively high stability and that theturntable rate 124 should be fairly high and that a low amount ofstretch wrap should be used.

FIG. 7A shows an example screen 42 that could be displayed on userinterface 24. Again the screen 42 shows the sustainability score. Thescreen 42 also shows a proposed turntable speed and a proposed amount ofstretch film to use. Each interface between two layers (and between thebottom layer and the pallet) is evaluated separately. Also, each layeris evaluated itself for gaps and height inconsistencies, which decreasestability. Because the stack of items on the pallet 50 in this exampleis cross-stacked plastic beverage crates (the bottle caps in one layersare received in recesses in the crates of the layer above), this is afairly stable stack. Further, the items in each layer are the sameheight.

Therefore, the computer 26 determines that the turntable speed isrelatively high (about 70%) and that only 30% stretch film is required.The computer 26 then controls the turntable 14 to rotate at the selectedspeed while the stretch film 20 is applied, and the computer 26 controlsthe vertical position of the roll of stretch film 20 while the turntable14 rotates. The computer 26 controls the turntable 14 and roll ofstretch film 20 to wrap the loaded pallet 50 as shown in FIG. 7A(including an X pattern on the sides), which is considered about a 30%stretch film 20 application.

An example with another loaded pallet 50 is shown on the screen 44 ofFIG. 7B. In this loaded pallet 50, the corrugated cardboard boxes in thebottom two layers do not interlock at all; nor do the cardboard trayswith plastic overwrap (with plastic bottles therein) interlock with oneanother; nor do the top layer of corrugated cardboard boxes on top ofthe cardboard trays with plastic overwrap (the top layers is also lessstable because it is the top layer). Further, some gaps are identifiedin the bottom two layers. There are no height discrepancies within anyof the layers. The pallet itself is determined to be stable.

Therefore, the computer 26 (or server 30) determines that the turntablespeed is relatively slow (about 25%) and that 90% stretch film coverageis required. The computer 26 then controls the turntable 14 to rotate atthe selected speed while the stretch film 20 is applied, and thecomputer 26 controls the vertical position of the roll of stretch film20 while the turntable 14 rotates. The number of times that theturntable 14 is rotated while the stretch film 20 is applied is alsodetermined by the computer 26 (or server 30) based upon the determinedstability, i.e. more turns (more layers of stretch film 20) for a lessstable loaded pallet 50. The computer 26 controls the turntable 14 androll of stretch film 20 to wrap the loaded pallet 50 as shown in FIG. 7B(including an X pattern on the sides with overlapping horizontal layersover it), which is considered about a 90% stretch film 20 application.

QC Bot

FIG. 8 shows a first embodiment of a QC bot having a QC interface 60implemented on a user interface device 62 such as a tablet having atouchscreen, processor(s), storage, wireless communication (as iswell-known—such as an iPad). Instructions in the storage would performthe functions described herein when executed by the processor(s). If thesystem 10 of FIG. 1 determines an error or potential error, then priorto wrapping, the system 10 directs the user to move the loaded pallet 50to a QC check area (FIG. 8 ).

The computer 26 sends the information regarding the error or potentialerror to the QC bot running on the user interface device 62. Thecomputer 26 also sends one or more of the images of the loaded pallet 50taken by camera 16 to the QC bot. The QC bot displays information on theQC interface 60 that will be useful to the QC worker.

For example, as shown in FIG. 8 , the QC interface 60 shows the QCworker an image 68 of the loaded pallet 50 with the error or potentialerror highlighted (e.g. by superimposing a box or outline around one ormore items). The image 68 may be one of the images taken duringvalidation by camera 14. In the example shown, a single item isidentified on the image 68 of the loaded pallet 50 with an instructionto “remove” that item along with a picture 69 of that item to be removed(either from a database of the expected items or a cropped portion ofthe image 68). Additional remedial steps may be instructed sequentially.

The QC interface 60 also shows some of the information that indicatedthe error (or potential error) and instructions for how to resolve theerror or potential error. The QC interface 60 also shows the QC workerthe pallet pick list 64 associated with that pallet 50 (i.e. thecomplete list of what should be on the pallet 50) and the loading dockdoor to which the pallet 50 should be taken after being checked/fixed.

In the specific example shown in FIG. 8 , the QC bot presents on theinterface 60 an information field 66 indicating: pallet id, the type ofproblem detected (e.g. extra item), the fact that the weight was 15 lbshigher than it should have been, the fact that the case count is one toohigh, and that the pallet 50 should be loaded at loading dock door 4.The complete pallet pick list 64 is also indicated on the screen.

After the QC worker remedies the problem, the QC worker takes anotherimage of the loaded pallet 50 (e.g. with the user interface device 62)and the QC bot 60 may revalidate the loaded pallet 50 or simply verifythat the instructions were followed and/or the case count is nowcorrect. Alternatively, the QC worker may return the loaded pallet 50 tothe wrapper of FIG. 1 to be revalidated and wrapped.

Referring to FIGS. 9 and 10 , the QC interface can also be implementedwith smart glasses 70 (such as Google Glass), which can provide the QCworker with the same information as in FIG. 8 but can also directly markthe item on the pallet 50 to be removed, such as via superimposition oflines outlining the item. In this case the QC bot may run on thecomputer 26 or on the server 30 or a dedicated QC computer communicatingwirelessly with the smart glasses 70.

FIGS. 11 and 12 show an alternate implementation of the QC bot. In thisembodiment, referring to FIG. 11 , a projector 164 is mounted near theQC station (e.g. overhead). The projector 164 could be an LED projectoror laser projector and includes a camera 166 or other device forreceiving input from the user.

As shown in FIG. 12 , the projector 164 projects the QC interfaceincluding the error notifications (or potential errors or unconfirmedSKUs) onto the pallet 50 and products 52 themselves. For example, inFIG. 12 , the projector 164 has projected “extra berry blue” directlyonto one of the products 52 on the pallet 50 (indicating that this item(a “berry blue”) should be removed). The projector 164 has alsoprojected “Not Zero Berry” onto another product 52 and “Not RaspberryLemonade” onto another (indicating that these items should be removedand replaced with the proper items, “Zero Berry” and “RaspberryLemonade”). The projected errors enable the user to be able to quicklysee what is wrong with the pallet.

Using gestures, such as touching one of the indicated products incombination with some particular gesture, the user can override or clearthe error after determining that the correct product is present. Or theuser can confirm the error and remove the product or remove and replacethe product and then provide a gesture (or other feedback, such asaudible) that the error has been corrected.

Any error that is cleared or updated will cause the software to updatelabels for active learning. For example, in response to the “Not ZeroBerry” error notification, the user could indicate (via gestures orverbal instructions over a headset) that the item is in fact “ZeroBerry.” This may occur if the packaging for Zero Berry has changed, orsimply because of an error by the server 30. Either way, the image ofthat item can then be used to train the machine learning model(s) 31 a-cin the server 30 so that the machine learning model(s) will recognizethat packaging as “Zero Berry” in the future.

Besides removing a product, the QC bot 60 may ask the QC worker toinspect and verify one or more products (if one or more items could notbe visually verified with sufficient confidence during validation),substitute one product for another, or add one or more products. The QCworker may provide inputs and feedback to the QC interface via spokencommands over a microphone on the smart glasses or via a headset.

Alternatively, a remote worker can audit a loaded pallet. The remoteworker can be presented with the images of the suspect items and canconfirm whether the suspect items are correct or incorrect. If theremote worker says that the suspect items are correct, the loaded palletis verified and no further action need be taken. If the remote workerindicates that the suspect items are a true error by the picker, then alocal worker can correct the error, or this information can be passed tothe driver for correction as indicated below.

Referring to FIGS. 13 and 14 , the audit history, including theinstructions and history of images of the pallet 50 are stored (e.g. inserver 30) in the event that there are ever any questions about theloaded pallet 50. In FIGS. 13 and 14 , the history is displayed on thedelivery person's mobile device, and indicated as “verified,” so thedelivery person can show this to the worker at the store where theloaded pallet 50 is being delivered. This can avoid the delivery personand the store worker having to verify the items being delivered, whichcan be time-consuming.

Alternatively, if the corrections are not actually made on the pallet,the data is updated for the driver so that the delivery driver can makethe correction at the store. It is easy for the driver to remove anextra item or two from a loaded pallet at the store. The delivery drivercould also have some extra stock in the truck to fill in any missingitems. Optionally, a pick list can be generated for a separate palletwith all of the items missing from the entire route. That separatepallet can be picked and loaded on that truck just before it leaves theloading dock.

In accordance with the provisions of the patent statutes andjurisprudence, exemplary configurations described above are consideredto represent a preferred embodiment of the invention. However, it shouldbe noted that the invention can be practiced otherwise than asspecifically illustrated and described without departing from its spiritor scope.

What is claimed is:
 1. A wrapping system comprising: a turntable forsupporting a stack of a plurality of packages; a camera configured totake a plurality of images of the stack of the plurality of packageswhile supported on the turntable; a supply of wrap adjacent theturntable; at least one processor; and at least one non-transitorycomputer-readable medium storing: at least one machine learning modelthat has been trained with a plurality of images of packages of beveragecontainers; and instructions that, when executed by the at least oneprocessor, cause the at least one processor to perform operationscomprising: a) receiving a plurality of images of a stack of a pluralityof packages of beverage containers on a pallet; b) inferring a packagetype of each the plurality of packages of beverage containers based uponthe plurality of images of the stack using the at least one machinelearning model; c) based upon inferred package types, determining astability of the stack; d) based upon the stability of the stack fromoperation c), determining how to wrap the stack of the plurality ofpackages of beverage containers; and e) controlling the turntable towrap the stack of the plurality of packages based upon operation d). 2.The wrapping system of claim 1 wherein the operation of controlling theturntable includes establishing a speed of rotation of the turntablebased upon the stability of the stack from operation c).
 3. The wrappingsystem of claim 1 wherein the operation of controlling the turntableincludes determining an amount of wrap to place around the stack of theplurality of packages based upon the stability of the stack fromoperation c).
 4. The wrapping system of claim 1 wherein thedetermination in operation d) includes determining an amount of wrap toplace around the stack of the plurality of packages.
 5. The wrappingsystem of claim 1 wherein the determination in operation d) includesdetermining a speed to wrap the stack of the plurality of packages. 6.The wrapping system of claim 1 wherein the operations further includemeasuring gaps between adjacent ones of the plurality of packages, andwherein operation c) is also performed based upon the measured gaps. 7.The wrapping system of claim 1 wherein operation c) further includesevaluating the package types of adjacent layers to evaluate an interfacebetween the adjacent layers and wherein determining a stability of thestack is based upon the interface.
 8. The wrapping system of claim 1wherein the operations further include evaluating alignment of packagesin adjacent layers including whether the packages in adjacent layers arecolumn-stacked, and wherein operation c) is performed based upon thealignment of packages in adjacent layers.
 9. A computing system forvalidating a stack of a plurality of packages comprising: at least oneprocessor; and at least one non-transitory computer-readable mediumstoring: at least one machine learning model that has been trained witha plurality of images of packages of beverage containers; andinstructions that, when executed by the at least one processor, causethe computing system to perform operations comprising: a) receiving atleast one image of the stack of the plurality of packages of beveragecontainers; b) inferring a SKU associated with each of the plurality ofpackages of beverage containers based upon the at least one image usingthe at least one machine learning model; c) comparing the inferred SKUsto an expected list of SKUs in an order; d) determining an error basedupon step c); and e) generating a display in which the error isindicated on one of the plurality of packages.
 10. The computing systemof claim 9 wherein operation e) further includes superimposing anindicator on one of the at least one image of the stack to indicate theerror.
 11. The computing system of claim 9 further including atranslucent display, wherein operation e) further includes superimposingan indicator on the translucent display in a position corresponding tothe one of the plurality of packages.
 12. The computing system of claim9 further including a projector, wherein operation e) further includesprojecting an indicator with the projector onto the one of the pluralityof packages.
 13. A computing system for imaging and evaluating a stackof a plurality of packages of beverage containers comprising: at leastone processor; and at least one non-transitory computer-readable mediumstoring: at least one machine learning model that has been trained witha plurality of images of packages of beverage containers; andinstructions that, when executed by the at least one processor, causethe computing system to perform operations comprising: a) receiving atleast one image of the stack of the plurality of packages of beveragecontainers; b) inferring a package type of each of the plurality ofpackages of beverage containers based upon the at least one image usingthe at least one machine learning model; and c) based upon the packagetype inferred for each of the plurality of packages of beveragecontainers, determining a sustainability rating.
 14. The computingsystem of claim 13 wherein the operations further include: d) displayingan image of the stack of the plurality of packages of beveragecontainers indicating the package types of the plurality of packages andrelative sustainability of the plurality of packages.
 15. The computingsystem of claim 13 wherein the operations further include: inferring apallet type of a pallet on which the stack of the plurality of imagesare supported based upon the at least one image using the at least onemachine learning model, wherein the sustainability rating is furtherbased upon the inferred pallet type.
 16. The computing system of claim13 wherein the operations further include: inferring a SKU of each ofthe plurality of packages based upon the at least one machine learningmodel and based upon the at least one image; and comparing the inferredSKUs for each of the plurality of packages with a plurality of expectedSKUs in an order.
 17. The computing system of claim 13 wherein theoperations further include: providing a user interface enabling a userto propose a change to the package types of at least one of theplurality of packages; and based upon the proposed change, indicating inthe user interface how the proposed change would change a relativesustainability of the plurality of packages.
 18. The computing system ofclaim 13 wherein the sustainability rating is based upon whether thepackages are reusable.
 19. A wrapping system comprising: a turntable forsupporting a stack of a plurality of packages; a camera configured totake a plurality of images of the stack of the plurality of packageswhile supported on the turntable; a supply of wrap adjacent theturntable; at least one processor; and at least one non-transitorycomputer-readable medium storing: at least one machine learning modelthat has been trained with a plurality of images of packages of beveragecontainers; and instructions that, when executed by the at least oneprocessor, cause the wrapping system to perform operations comprising:a) receiving a plurality of images of a stack of a plurality of packagesof beverage containers on a pallet; b) inferring a package type of eachthe plurality of packages of beverage containers based upon theplurality of images of the stack using the at least one machine learningmodel; c) evaluating alignment of packages in adjacent layers includingwhether the packages in adjacent layers are column-stacked; d)determining gaps between adjacent ones of the plurality of packages; e)based upon inferred package types, the gaps and the alignment,determining a stability of the stack; f) based upon the stability of thestack from operation e), determining how to wrap the stack of theplurality of packages of beverage containers; and g) controlling theturntable to wrap the stack of the plurality of packages based uponoperation e), including establishing a speed of rotation of theturntable based upon the stability of the stack from operation e) anddetermining an amount of wrap to place around the stack of the pluralityof packages based upon the stability of the stack from operation e). 20.The wrapping system of claim 19 wherein the operations further include:based upon the package type inferred for each of the plurality ofpackages of beverage containers, determining a sustainability rating;and displaying an image of the stack of the plurality of packages ofbeverage containers indicating the package types of the plurality ofpackages and relative sustainability of the plurality of packages.